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High-resolution remote sensing extraction of urban buildings based on morphological sequences and multi-source a priori information

  • role： First author第一作者
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

  • Email：lizhi@lreis.ac.cn
  • Introduction：李治，研究方向为多源遥感信息协同提取与城市生态应用。E-mail：lizhi@lreis.ac.cn
LI Zhi,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

SUI Zhengwei,  
  • role： Corresponding author通信作者
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

  • Email：fuqiaoyan1970@163.com
  • Introduction：傅俏燕，研究方向为资源环境遥感技术与应用。E-mail： fuqiaoyan1970@163.com
FU Qiaoyan*,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

ZHENG Jinjin,  
  • Affiliation：

    China Center for Resources Satellite Date and Application, Beijing 100094, China

BU Tong

résumé

Urban building extraction is an important research direction for the understanding and target recognition of high-resolution optical remote sensing images. Realizing accurate automatic building extraction has important application value and practical significance for the acquisition and update of basic urban geographic information. Given the complexity of urban scenes and the diversity of building forms, the characteristics of urban buildings are difficult to express fully, and the generalization ability of samples is insufficient, thus becoming a bottleneck problem for the automatic extraction of urban buildings. In this study, a multi-modal morphological-sequence-feature synergy method is proposed to utilize fully the advantages of each morphological sequence feature from different modes and mine the high-dimensional spatial information of urban buildings jointly. On this basis, we introduce multi-source a priori information and develop an adaptive segmentation model method based on multi-source a priori information to achieve the automatic recognition of urban buildings. This method can help avoid the limitations, such as errors and low efficiency, brought by manual threshold selection.The process of the method for urban building extraction proposed in this study is mainly divided into four steps. First, the differential morphological structure sequence features and differential morphological attribute sequence features of remote sensing images are calculated on the basis of high-resolution remote sensing images. Second, the feature selection model is constructed to optimize the differential morphological structure sequence features and differential morphological attribute sequence features. Then, the adaptive segmentation model is constructed on the basis of the multi-source a priori information products. The adaptive segmentation of the preferred features is performed to obtain the initial information of urban buildings. Finally, the voting method is used to fuse the initial information of urban buildings at the decision level to obtain the final urban building extraction results.The performance of the proposed method shows that the average extraction accuracy and kappa coefficient of the research method in this study are 91.3% and 0.87, which are 7.8% and 5.5% and 0.1 and 0.07 higher than the 85.7% and 83.5% and 0.81 and 0.78 of DMPs and DAPs extraction methods, respectively. Thus, the results demonstrate the effectiveness of the method in the automatic extraction of urban buildings in this study.The method in this research achieves rapid, automated, and high-precision urban building information acquisition and update. Furthermore, it provides a method reference template for rapid building detection and update in more cities. In the subsequent research, further quantitative evaluation of each type of a priori information product is needed to clarify the role of different information products in automatic building extraction, as it can improve the accuracy and automation of building extraction further.

mots-clés

Morphological structural sequence features;morphological attribute sequence features;feature significance level model;adaptive segmentation model;decision-level information fusion;a priori information

References

  1. 1.
    Cavallaro G, Falco N, Mura M D and Benediktsson J A. 2017. Automatic attribute profiles. IEEE Transactions on Image Processing, 26(4): 1859-1872
  2. 2.
    Cué La Rosa L E, Queiroz Feitosa R, Nigri Happ P, Del’Arco Sanches I and Ostwald Pedro da Costa G A. 2019. Combining deep learning and prior knowledge for crop mapping in tropical regions from multitemporal SAR image sequences. Remote Sensing, 11(17): 2029
  3. 3.
    Dalla Mura M, Benediktsson J A, Waske B and Bruzzone L. 2010. Morphological attribute profiles for the analysis of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 48(10): 3747-3762
  4. 4.
    Du P J. 2020. Progress of high resolution remotely sensed image progressing and urban application examples. Modern Surveying and Mapping, 43(1): 1-9
  5. 5.
    Du P J, Bai X Y, Luo J Q, Li E Z and Lin C. 2018. Advances of urban remote sensing. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 10(1): 16-29
  6. 6.
    Fauvel M, Benediktsson J A, Chanussot J and Sveinsson J R. 2008. Spectral and spatial classification of Hyperspectral data Using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46(11): 3804-3814
  7. 7.
    Geiß C, Klotz M, Schmitt A and Taubenböck H. 2016. Object-based morphological profiles for classification of remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 5952-5963
  8. 8.
    Huang X and Zhang L P. 2012. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1): 161-172
  9. 9.
    Li D R. 2018. Brain cognition and spatial cognition: on integration of geo-spatial big data and artificial intelligence. Geomatics and Information Science of Wuhan University, 43(12): 1761-1767
  10. 10.
    Li J J, Cao J N, Feyissa M E and Yang X Q. 2020. Automatic building detection from very high-resolution images using multiscale morphological attribute profiles. Remote Sensing Letters, 11(7): 640-649
  11. 11.
    Lin X G and Zhang J X. 2017. Object-based morphological building index for building extraction from high resolution remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(6): 724-733
  12. 12.
    Liu C R, Frazier P and Kumar L. 2007. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107(4): 606-616
  13. 13.
    Lv Z Y, Zhang P L, Benediktsson J A and Shi W Z. 2014. Morphological profiles based on differently shaped structuring elements for classification of images with very high spatial resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12): 4644-4652
  14. 14.
    Ma W X, Wan Y C, Li J Y, Zhu S and Wang M W. 2019. An automatic morphological attribute building extraction approach for satellite high spatial resolution imagery. Remote Sensing, 11(3): 337
  15. 15.
    Marpu P R, Pedergnana M, Mura M D, Peeters S, Benediktsson J A and Bruzzone L. 2012. Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques. International Journal of Image and Data Fusion, 3(3): 269-298
  16. 16.
    Pesaresi M, Ouzounis G K and Gueguen L. 2012. A new compact representation of morphological profiles: report on first massive VHR image processing at the JRC//Proceedings of SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore: SPIE: 839025
  17. 17.
    Su Z H, Liang Q O and Zhu L J. 2015. Comparison of classification accuracy of remote sensing imagery base on different morphological profiles. Remote Sensing Information, 30(2): 36-42
  18. 18.
    Wang C, Shen Y, Liu H, Zhao K G, Xing H Y and Qiu X. 2019. Building extraction from high–resolution remote sensing images by adaptive morphological attribute profile under object boundary constraint. Sensors, 19: 3737
  19. 19.
    Wang C and Wang X. 2021. Building change detection from multi-source remote sensing images based on multi-feature fusion and extreme learning machine. International Journal of Remote Sensing, 42(6): 2246-2257
  20. 20.
    Wang J, Qin Q M, Ye X, Wang J H, Qin X B and Yang X C. 2016. A survey of building extraction methods from optical high resolution remote sensing imagery. Remote Sensing Technology and Application, 31(4): 653-662, 701
  21. 21.
    Wei D S and Zhou X G. 2019. Automatic sampling of remote sensing image change detection samples based on prior information of vector data. Journal of Remote Sensing, 23(3): 464-475
  22. 22.
    You Y F, Wang S Y, Ma Y X, Chen G S, Wang B, Shen M and Liu W H. 2018. Building detection from VHR remote sensing imagery based on the morphological building index. Remote Sensing, 10(8): 1287
  23. 23.
    Zhang Y Y, Fei X Y, Wang J, Wang X X and Chen Z. 2020. Survey of building extraction methods based on high resolution remote sensing images. Geomatics and Spatial Information Technology, 43(4): 76-79

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